Improving Digital Cancer Care for Older Black Adults: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Health systems are rapidly promoting digital cancer care models to improve cancer care of their populations. However, there is growing evidence that digital cancer care can exacerbate inequities in cancer care for communities experiencing social disadvantage, such as Black communities. Despite the increasing recognition that older Black adults face significant challenges in accessing and using health care services due to multiple socioeconomic and systemic factors, there is still limited evidence regarding how older Black adults' access and use digital cancer care. OBJECTIVE: This study aims to better understand the digital cancer care experience of older Black adults, their caregivers, and health care providers to identify strategies that can better support patient-centered digital cancer care. METHODS: A total of 6 focus group interviews were conducted with older Black adults living with cancer, caregivers, and health care providers (N=55 participants) across 10 Canadian provinces. Focus group interviews were recorded and transcribed. Through a theory-informed thematic analysis approach, experienced qualitative researchers used the Patient Centered Care model and the synergies of oppression conceptual lens to inductively and deductively code interview transcripts in order to develop key themes that captured the digital cancer care experiences of older Black adults. RESULTS: In total, 5 overarching themes describe the experience of older Black adults, caregivers, and health care providers in accessing and using digital cancer care: (1) barriers to access and participation in digital care services, (2) shifting caregivers' dynamics, (3) autonomy of choice and choosing based on the purpose of care, (4) digital accessibility, and (5) effective digital communication. We identify 8 barriers and 6 facilitators to optimal digital cancer for older Black adults. Barriers include limited digital literacy, linguistic barriers in traditional African or Caribbean languages, and patient concerns of shifting power dynamics when supported by their children for digital cancer care; and facilitators include community-based cancer support groups, caregiver support, and key features of digital technologies. CONCLUSIONS: These findings revealed a multifaceted range of barriers and facilitators to digital cancer care for older Black adults. This means that a multipronged approach that simultaneously focuses on addressing barriers and leveraging community strengths can improve access and usage of digital cancer care. A redesign of digital cancer care programs, tailored to the needs of most structurally marginalized groups like older Black adults, can enhance the digital care experience for all population groups. Public policies and organizational practices that address issues like availability of internet in remote areas, resources to support linguistic barriers, or culturally sensitive training are important in responding to the complexity of access to digital l cancer care. These findings have implications for other structurally marginalized and underresourced communities that have suboptimal access and usage of digital care.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,005 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».